A method for discriminating original pulses in online partial discharge measurement

Allahbakhshi, Mehdi; Akbari, Asghar

doi:10.1016/j.measurement.2010.09.036

Cited by 32 publications

(11 citation statements)

References 20 publications

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“…Its early diagnosis gives possibility to avoid a complete breakdown of an affected component of a power supply system. Usual measurement systems of partial discharges have some difficulty in measuring, especially in online environments and in noisy environments [6].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Designing a combined device for determining the place of arc discharge

Parkhomenko

Aniskov

Tsibulevsky³

et al. 2018

EEJET

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Designing a combined device for determining the place of arc discharge

Parkhomenko

Aniskov

Tsibulevsky³

et al. 2018

EEJET

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“…A PD sensor (Rogowski coil in this work) is designed for a certain bandwidth which is suitable for PD measurements. The bandwidth of noise lies well above the bandwidth of PD signals [25]. Therefore output of a Rogowski coil is a well-defined signature (shape) for a PD signal.…”

Section: Detectionmentioning

confidence: 99%

Electromagnetic sensing for predictive diagnostics of electrical insulation defects in MV power lines

et al. 2015

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“…One of the methods involves setting a threshold and ignoring PD data that are 10% of the maximum PD amplitude. However, it was found to be insufficient as high threshold level might neglect real PD pulses with low magnitude and low threshold level will include noise [24, 25]. Using the mean square error as a benchmark to compare 28 types of denoising technique, wavelet based denoising was found to be the best with good signal to noise ratio [26].…”

Section: Introductionmentioning

confidence: 99%

Classification of Partial Discharge Measured under Different Levels of Noise Contamination

2017

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Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.

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A method for discriminating original pulses in online partial discharge measurement

Cited by 32 publications

References 20 publications

Designing a combined device for determining the place of arc discharge

Designing a combined device for determining the place of arc discharge

Electromagnetic sensing for predictive diagnostics of electrical insulation defects in MV power lines

Classification of Partial Discharge Measured under Different Levels of Noise Contamination

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